Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity

Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, Yi Sun


Abstract
Ensuring relevance quality in product search is a critical task as it impacts the customer’s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.
Anthology ID:
2022.ecnlp-1.6
Volume:
Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | ECNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–48
Language:
URL:
https://aclanthology.org/2022.ecnlp-1.6
DOI:
10.18653/v1/2022.ecnlp-1.6
Bibkey:
Cite (ACL):
Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, and Yi Sun. 2022. Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity. In Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 44–48, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity (Bagheri Garakani et al., ECNLP 2022)
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PDF:
https://aclanthology.org/2022.ecnlp-1.6.pdf